AI & Manufacturing
An Automatic Method for Machining Process Route Generation Based on Large Language Models and Retrieval-Augmented Generation
This paper presents an innovative approach to automating machining process route generation in aviation manufacturing. By integrating Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) frameworks, the method significantly enhances the efficiency and quality of process planning. It addresses limitations of traditional CAPP systems by dynamically updating knowledge bases and leveraging AI algorithms for intelligent process route and operation order generation. This intelligent solution is crucial for meeting the increasing demands for precision and speed in modern manufacturing.
Revolutionizing Aviation Manufacturing with AI
The integration of LLM and RAG into CAPP systems promises a significant leap in productivity and accuracy for aviation manufacturing. By automating complex planning tasks, it frees up expert engineers to focus on innovation and complex problem-solving, leading to faster development cycles and higher quality outputs across the industry.
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Efficiency Increase in Process Planning
Deep Analysis & Enterprise Applications
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This paper presents an innovative approach to automating machining process route generation in aviation manufacturing. By integrating Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) frameworks, the method significantly enhances the efficiency and quality of process planning. It addresses limitations of traditional CAPP systems by dynamically updating knowledge bases and leveraging AI algorithms for intelligent process route and operation order generation. This intelligent solution is crucial for meeting the increasing demands for precision and speed in modern manufacturing.
LLM & RAG
Core Technologies for Automated Generation
Automated Process Route Generation Workflow
Part Info & Requirements
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Query Formulation (LLM)
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Knowledge Retrieval (RAG)
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Process Route Generation
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Operation Order Generation
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Verification & Optimization
| Feature | Traditional CAPP | AI-Driven CAPP (LLM+RAG) |
|---|---|---|
| Knowledge Source | Static, Manual Rules | Dynamic, Vector DB, LLM |
| Planning Method | Retrieval-based, Rule-based | Retrieval-Augmented Generative |
| Efficiency | Moderate, Time-consuming | High, ~10% improvement |
| Adaptability | Limited to known parts | High, generalizes to new parts |
| Error Rate | Human Error, Inconsistency | Reduced Hallucinations (RAG) |
| Optimization | Manual, Iterative | Continuous via Feedback Loop |
Aviation Manufacturing Application
In a critical application within aviation manufacturing, this AI-driven CAPP system successfully planned the machining process routes for complex fuselage components. The system demonstrated a 10% reduction in planning time compared to traditional methods, while also enhancing precision and ensuring compliance with stringent aerospace standards. This project highlighted the potential of LLM and RAG to handle high-complexity tasks, especially with dynamic updates to the knowledge base ensuring the latest manufacturing standards are always applied.
Outcome: Improved process planning efficiency and quality in high-stakes aerospace projects.
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Estimated Annual Savings
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Hours Reclaimed Annually
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Implementation Roadmap
Our phased approach ensures a smooth, secure, and successful integration of AI into your enterprise, maximizing ROI with minimal disruption.
Data Harmonization & Knowledge Base Construction
Establish a unified data format, preprocess historical data, and build both process route and operation order knowledge bases using Milvus for vector storage.
LLM & RAG Model Integration
Configure DeepSeek-R1-32B and Qwen2.5-32B for generation, and BGE-M3 with BGE-reranker-v2-m3 for embeddings and reranking, within a LangChain pipeline.
Automated Process Route & Operation Order Generation
Implement the two-stage generation process: query formulation, knowledge retrieval, answer generation with Few-Shot Prompting, and initial verification.
Continuous Optimization & Expert Feedback Loop
Integrate a human feedback mechanism to capture expert corrections, dynamically update knowledge bases, and continuously improve model performance and accuracy.
Pilot Deployment & Scalability Assessment
Roll out the system in a pilot manufacturing environment, monitor performance, gather user feedback, and assess scalability for broader enterprise integration.
The LLM and RAG framework offers a powerful solution for automating complex process planning in aviation manufacturing, delivering enhanced efficiency and reliability. Its dynamic adaptability and intelligent generation capabilities are poised to transform industrial manufacturing workflows, making it an indispensable tool for future digital transformation initiatives. The ability to seamlessly integrate into existing CAPP software underscores its practical applicability and immense potential for innovation.
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